Sensors that operate in the presence of a noise-floor (either due to external noise or noise caused by the sensor) need to be configured or have readout strategies that allow the detection of very small target signals. Standard techniques for accounting for such noise include using a long observation time to collect a large amount of data which can then be suitably averaged to yield, for instance, a power spectral density that is almost noise-free. Typically, mathematical algorithms, such as fast Fourier Transforms, are used to produce such averaged results. It is generally accepted, however, that the human brain does not compute fast Fourier transforms. In addition, the brain carries out tasks such as pattern recognition in a minimal amount of time (usually a few msec). While there have been investigations into “noise shaping” in biological neurons, there have not yet been any applications to nonlinear dynamic sensors. Noise shaping reduces the noise floor in low-frequency signals, thereby rendering them more easily detectable. It would be desirable to configure a sensor such that it is able to provide measurements with optimal accuracy, such as those achieved by the brain.